Iterative image reconstruction methods, such as non-negative least square or likelihood algorithms, iteratively fit image models to a data set and thus calculate a final image while minimizing the effect of noise to the image. An overview of different reconstruction methods is given in R. C. Puetter et al., “Digital Image Reconstruction: Deblurring and Denoising,” Annu. Rev. Astro. Astrophys., 2005, 43: 139-194, the contents of which are herein incorporated by reference. One example for efficient reconstruction is a non-negative least squares fit (NNLS). Another example is an ordered subset expectation maximization algorithm (OSEM algorithm), which is described, for example, in H. M. Hudson and R. S. Larkin, “Accelerated image reconstruction using ordered subsets of projection data,” IEEE Transactions on Medical Imaging, vol. 13, no. 4, pp. 601-609, 1994, the contents of which are herein incorporated by reference.
Within the OSEM algorithm, an iteration step is defined as a single pass through all the subsets, in each subset using the current estimate to initialize application of the expectation maximization with the data subset. As the OSEM algorithm does not converge and may cycle, the user typically predefines the number of iterations. If the number of iterations is set too low, the reconstruction is incomplete, i.e., a loss of resolution is retained. However, if the number is set to high, the reconstruction takes too long and may yield artifacts. Usually, the applied number of iterations is set on the basis of experimentation with the current data or according to the reconstruction of a similar data set previously processed.